AI-Powered Master Data Management in Healthcare-Enhancing Integration, Decision-Making, and Supply Chain Resilience

Master data management (MDM) is foundational to the reliability, uniformity, and protection of healthcare data, which underpins decisive, efficient operations. Recent advancements in AI, particularly machine learning, are modernizing MDM practices in data merging, governance structuring, and supply chain oversight. This paper illustrates AI’s contribution to MDM by automating the cleansing of heterogeneous data, harmonizing clinical records, and bridging disparate health IT systems without human latency. The research further explores predictive analytics orchestrated by AI to oversee the supply chain in real time, guaranteeing uninterrupted visibility, optimized workflows, and resilience to supply shocks. AI’s contribution to automated compliance with GDPR, HIPAA, and MDR is also deliberated, highlighting reduced risk exposure and fortified patient data protection. Leveraging multiple in-field implementations, we quantify how AI-infused MDM environments drive lower total data ownership costs, empower superior clinical judgement, and refine operational throughput in hospitals. Results validate that next-generation, AI-equipped MDM strategies are not merely advantageous but necessary, positioning health systems to operate efficiently, remaining audit-compliant, and scale capabilities in line with patient and regulatory demand.